Anthropic Claude Sonnet 4.5: File Upload & Reading

Anthropic Claude Sonnet 4.5 expands file upload and reading capabilities into a complete document-processing ecosystem built for long-context workloads, multi-file operations, and developer environments that require reliable ingestion of PDFs, spreadsheets, text archives, and structured datasets.

As part of the Claude 4.5 model family, Sonnet 4.5 combines high-quality reasoning with fast file parsing, persistent file references, tool execution pipelines, and long-running session management that supports multi-step workflows across several documents.

Its architecture supports dedicated file storage through the Files API, seamless referencing of uploaded files across turns, and extended context windows capable of analysing full reports, legal contracts, financial statements, codebases, and multi-document datasets in a single session.

The result is a model positioned for deep enterprise workflows, agentic orchestrations, and large-scale document analysis that requires accurate extraction, transformation, comparison, and structured comprehension.

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Claude Sonnet 4.5 supports persistent file upload through the Files API for reusable, long-running document workflows.

Claude Sonnet 4.5 introduces a structured Files API that allows developers to upload documents once, receive a unique file ID, and reference that file repeatedly across Messages API calls without re-sending content.

This approach enhances performance and reduces token usage in multi-step workflows where the model must repeatedly access the same document across planning, extraction, transformation, and verification tasks.

The Files API supports documents, images, and general container uploads, enabling PDFs, text files, CSVs, images with embedded text, and multi-page files to be analysed as part of the same ongoing session.

By decoupling file content from message payloads, Sonnet 4.5 creates a stable environment for long-form ingestion, structured reasoning, and iterative refinement across several turns, all while maintaining continuity through file references.

This structure is particularly powerful for enterprise document systems, where a single workflow may include onboarding a file, extracting tables, generating reports, creating summaries, comparing versions, and exporting structured results.

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File Upload Architecture

Component	Behavior in Sonnet 4.5	Operational Advantage
Files API	Upload files with persistent IDs	Avoids repeated uploads
Document Blocks	PDF, text, and structured types supported	Flexible ingestion
File Referencing	Attach file_id inside messages	Maintains continuity
Cross-Turn Access	Files reused over multiple messages	Enables long workflows
Storage Lifecycle	Files listable, retrievable, deletable	Controlled governance

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Sonnet 4.5 uses long context windows to analyse large PDFs, spreadsheets and multi-document sets in a single continuous session.

One of the core strengths of Claude Sonnet 4.5 is its expanded long-context capability, which supports extremely large text inputs, multi-document ingestion, and multi-stage reading tasks within the same context window.

With token capacities that enable hundreds of pages of content to remain active during a session, Sonnet 4.5 can read, summarise, segment, extract, transform, and compare multiple files without discarding earlier information prematurely.

Sonnet 4.5’s sliding-window memory model ensures that as long as the combined file content and conversation remain within the token limit, the model maintains awareness of earlier sections, figures, tables, and page references across long interactions.

This makes the model suitable for processing:full annual reports, regulatory filings, legal documents, research papers, technical specifications, code directories, or multi-file datasets that require coherent cross-analysis.

With multiple file references active simultaneously, Sonnet can compare documents, track differences, merge datasets, and produce aggregated insights across all available materials.

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Long-Context Document Handling

Dimension	Sonnet 4.5 Implementation	Practical Outcome
Token Window	Very large multi-hundred-page capacity	Full reports can be analysed
Sliding Memory	Keeps recent context active	Supports iterative workflows
Multi-File Load	Several files referenced together	Cross-document analysis
In-Context Stability	Maintains structural awareness	Accurate extraction
Extended Sessions	Long conversations supported	Fits enterprise processes

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Claude Sonnet 4.5 offers advanced document reading, extraction, comparison and structured transformation for uploaded files.

The reading engine inside Sonnet 4.5 supports a wide spectrum of document-processing tasks that rely on structured reasoning across text, tables, sections, and mixed-media components inside uploaded files.

The model can ingest full PDFs, identify text blocks, extract tables, recognise headings, follow section references, and convert embedded data into machine-readable formats such as JSON, CSV, Markdown, or regenerated tables.

It can compare two files line-by-line or concept-by-concept, highlight changes, identify inserted or removed clauses, and produce consolidated reports describing differences.

For spreadsheets or tabular text, Sonnet 4.5 can extract rows and columns, transform data types, detect summaries, create pivot-like interpretations, or regenerate structured outputs tailored for further automated processing.

This enables developers to construct workflows that read a file, break its structure, perform targeted extraction, generate new documents, and validate outputs inside the same long-running session.

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Document Processing Capabilities

Capability	Functionality in Sonnet 4.5	Application
PDF Reading	Full-file ingestion and section parsing	Compliance, research
Table Extraction	Identify, convert, format tables	KPI reporting
Cross-File Comparison	Highlight changes and differences	Contract versioning
Text Transformation	Convert files to JSON, CSV, Markdown	Data pipelines
Hybrid Reasoning	Combine analysis across files	Consolidated summaries

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The Files API integrates with tool calling and agentic workflows for deeper automation and programmable document pipelines.

Claude Sonnet 4.5 is built to work inside the broader Anthropic tool-calling and agent ecosystem, enabling uploaded files to serve as inputs for multi-step reasoning, code execution, and automated workflow planning.

Through tool schemas and function calling, Sonnet can reference uploaded files, decide which tool to call, pass file content to the tool execution layer, incorporate returned data, and continue with the reasoning chain.

This enables pipelines such as:file ingestion → extraction → call to external system → generate new file → validate → update downstream storage.

Developers can also combine file uploads with code execution workflows to produce spreadsheets, transform datasets, or generate structured artifacts using programmatic logic guided by the model.

The workflow extends to enterprise environments through integrations with AWS Bedrock, Google Cloud Vertex AI, and other platforms that support Claude Sonnet 4.5 with cloud-native orchestration, secure authentication, and large-scale runtime environments.

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File-Driven Workflow Integration

Layer	Role in File Processing	Outcome
Tool Calling	Model selects and runs tools based on file context	Automates multi-step tasks
Function Calling	Structured schema outputs	Reliable formatting
External Systems	API calls, storage, databases	Enterprise integration
Code Execution	Logic-driven transformations	Scripted document workflows
Agent Orchestration	Multi-step planning	End-to-end process automation

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Effective file upload and reading with Sonnet 4.5 requires proper document preparation, lifecycle management and structured prompting.

To obtain high-quality analysis and extraction from Sonnet 4.5, developers should prepare documents and manage the upload lifecycle with precision to ensure optimal context usage and stable interpretation.

Searchable text-based PDFs, structured CSV extracts, and well-formatted text files provide the most reliable results and allow the model to identify data hierarchies, table boundaries, and logical sections with high accuracy.

Scanned or image-heavy PDFs may reduce quality; converting them to text or splitting them into structured subsections improves interpretability and reduces token overhead.

Prompt instructions should reference file IDs, page ranges, and section names so that Sonnet maintains focus and aligns its reasoning with the correct parts of the document.

Enterprises should manage file storage, retention, deletion and versioning to maintain governance and ensure that model-accessible files remain traceable and compliant with internal policies.

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Best-Practice Guidance

Area	Recommendation	Benefit
Document Format	Use text-based or OCR-processed files	Higher extraction accuracy
Size Control	Split documents exceeding token limits	Avoid truncation
Prompt Precision	Reference file_id, pages, sections	Better targeting
Lifecycle Management	Upload once, reuse, delete when done	Efficiency and compliance
Structured Outputs	Request JSON/CSV/Markdown	Automated pipelines

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